{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/alhichri/Heartprint/blob/main/Heartprint_biometrics_dataset_classification_tensorflow.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dpJJ-vZBf3hT",
        "outputId": "14fd8c6a-e4f0-4b21-e3e7-88dabe4c402b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "TensorFlow version: 2.8.2\n"
          ]
        }
      ],
      "source": [
        "from concurrent.futures import ProcessPoolExecutor\n",
        "\n",
        "import cv2\n",
        "import scipy.io\n",
        "import joblib\n",
        "import numpy as np\n",
        "import pywt\n",
        "import shutil\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from skimage.transform import resize\n",
        "\n",
        "import tensorflow as tf\n",
        "print(f\"TensorFlow version: {tf.__version__}\")\n",
        "#import keras (high level API) wiht tensorflow as backend\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.layers import AveragePooling2D, Dropout, Flatten, Dense, Input\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.models import Model , Sequential\n",
        "\n",
        "from tensorflow.keras.layers import GlobalAveragePooling1D, LayerNormalization, Permute, Layer, Input, Add, Dense, \\\n",
        "    Flatten, Activation, Lambda, Conv2D, BatchNormalization, Conv3D, Layer, MaxPooling2D\n",
        "from tensorflow.keras import layers, activations\n",
        "from tensorflow.keras.applications import ResNet50, ResNet101, ResNet152, VGG16, VGG19\n",
        "from tensorflow.keras.applications import EfficientNetB0, EfficientNetB3, efficientnet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9e8-EkUbSYSZ"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qTWodclrSfP2",
        "outputId": "e78a759e-3bc1-411f-cc4b-f078da002493"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive' , force_remount=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4iwUN4lYTOzX"
      },
      "outputs": [],
      "source": [
        "from importlib.machinery import SourceFileLoader\n",
        "from importlib.util import spec_from_loader, module_from_spec\n",
        "\n",
        "library_path = '/content/gdrive/MyDrive/000research/13-INF2168-02-R/colab_code/ECG-Classification-Using-CNN-and-CWT/EfficientBigEarthNet-main/'\n",
        "\n",
        "somemodule = SourceFileLoader('gradcam', library_path+'gradcam.py').load_module()\n",
        "from gradcam import GradCAM\n",
        "\n",
        "somemodule = SourceFileLoader('inputs', library_path+'inputs.py').load_module()\n",
        "from inputs import create_batched_dataset\n",
        "\n",
        "somemodule = SourceFileLoader('modules', library_path+'modules.py').load_module()\n",
        "from modules import se_module, cbam_module, coord_module, eca_module, ghost_module\n",
        "\n",
        "somemodule = SourceFileLoader('metrics', library_path+'metrics.py').load_module()\n",
        "from metrics import CustomMetrics\n",
        "\n",
        "somemodule = SourceFileLoader('models', library_path+'models.py').load_module()\n",
        "from models import MODELS_CLASS\n",
        "import models\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sq-ZkPRaSuGO",
        "outputId": "ea25dfbb-833c-4120-edd0-201e17a1eae4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "dict_keys(['__header__', '__version__', '__globals__', 'hbs'])\n",
            "(2, 199)\n",
            "220\n",
            "(8205, 227) (8205,)\n",
            " NUM_CLASSES:  221\n",
            " number of unique classes:  (199,)\n",
            "Train class sizes:  [ 29.   0.  40.  32.  30.  33.  38.  35.  28.  34.  29.  33.  35.  33.\n",
            "  33.  47.  40.  29.  40.  28.  41.  35.  23.  30.  30.  39.  30.  31.\n",
            "  42.  33.  34.  29.  46.  26.  24.  30.  33.  33.  44.  32.  39.  33.\n",
            "  25.  30.  42.  31.  31.  23.  20.  23.  35.  28.  31.  35.  22.  32.\n",
            "  38.  42.  44.  39.  44.  47.  40.  39.  36.  34.  37.  32.  37.  45.\n",
            "  42.  37.  32.  40.  31.  36.  33.  29.  35.  35.  25.  29.  26.  29.\n",
            "  32.  30.  28.  30.  31.  44.  40.  38.  37.  30.  30.  35.  33.  30.\n",
            "  25.  34.  43.  35.  31.  46.  34.  32.  35.  45.  35.  44.  38.  45.\n",
            "  29.  40.  31.  32.  28.  28.  41.  37.  31.  25.  32.  36.  39.  32.\n",
            "  37.  26.  32.  27.  35.  30.  35.  40.  30.  32.  37.  29.  32.  35.\n",
            "  33.  32.  34.  44.  24.  28.  28.  36.  41.  28.  42.  38.  30.  31.\n",
            "  28.  35.  31.  39.  29.  37.  30.  36.  42.  32.  38.  31.  27.  28.\n",
            "  33.   0.   0.  72.   0.   0.  66.  69.   0.  44.  53.   0.   0.   0.\n",
            "  66.  74.   0.   0.  59.  32.  92.   0.   0.  91.  83.  92.  75.   0.\n",
            "  63.  61.  93.  57. 131.  68.   0. 119.  94. 129.  68.   0. 114.  54.\n",
            "  56.   0. 101.   0.   0. 115.   0.   0.   0. 126. 111.] Total:  8205.0\n",
            " NUM_CLASSES:  199\n",
            " number of unique classes:  (199,)\n",
            "Train class sizes:  [ 29.  40.  32.  30.  33.  38.  35.  28.  34.  29.  33.  35.  33.  33.\n",
            "  47.  40.  29.  40.  28.  41.  35.  23.  30.  30.  39.  30.  31.  42.\n",
            "  33.  34.  29.  46.  26.  24.  30.  33.  33.  44.  32.  39.  33.  25.\n",
            "  30.  42.  31.  31.  23.  20.  23.  35.  28.  31.  35.  22.  32.  38.\n",
            "  42.  44.  39.  44.  47.  40.  39.  36.  34.  37.  32.  37.  45.  42.\n",
            "  37.  32.  40.  31.  36.  33.  29.  35.  35.  25.  29.  26.  29.  32.\n",
            "  30.  28.  30.  31.  44.  40.  38.  37.  30.  30.  35.  33.  30.  25.\n",
            "  34.  43.  35.  31.  46.  34.  32.  35.  45.  35.  44.  38.  45.  29.\n",
            "  40.  31.  32.  28.  28.  41.  37.  31.  25.  32.  36.  39.  32.  37.\n",
            "  26.  32.  27.  35.  30.  35.  40.  30.  32.  37.  29.  32.  35.  33.\n",
            "  32.  34.  44.  24.  28.  28.  36.  41.  28.  42.  38.  30.  31.  28.\n",
            "  35.  31.  39.  29.  37.  30.  36.  42.  32.  38.  31.  27.  28.  33.\n",
            "  72.  66.  69.  44.  53.  66.  74.  59.  32.  92.  91.  83.  92.  75.\n",
            "  63.  61.  93.  57. 131.  68. 119.  94. 129.  68. 114.  54.  56. 101.\n",
            " 115. 126. 111.] Total:  8205.0\n"
          ]
        }
      ],
      "source": [
        "dataset_path = '/content/gdrive/MyDrive/000research/13-INF2168-02-R/Data-ECG/PP-HB-199/'\n",
        "\n",
        "mat = scipy.io.loadmat(dataset_path+'hb_p9_s1.mat')\n",
        "# X = mat['X']; y = mat['y']; NUM_CLASSES = int(y.max()+1); print(NUM_CLASSES)\n",
        "print(mat.keys())\n",
        "h = mat['hbs']\n",
        "print(h.shape)\n",
        "number_of_subjects = h.shape[1]\n",
        "\n",
        "# we have a problem with subject 145  h[0,145]  has shape 0,0\n",
        "X = h[0 , 0] ; Y = np.zeros( ( X.shape[0])  ); \n",
        "label= 1\n",
        "for s in range(1,number_of_subjects):\n",
        "  h0 = h[0 , s]; label = h[1 , s][0][0]\n",
        "  # print( 'heartbeats: ' , h0.shape , ' label: ', s)\n",
        "  # if h0.shape[0]>0:\n",
        "  X = np.vstack((X , h0))\n",
        "  labels = label * np.ones( ( h0.shape[0]));     \n",
        "  Y = np.hstack((Y ,  labels   ))\n",
        "  # label = label + 1\n",
        "NUM_CLASSES = int(label); print(NUM_CLASSES) \n",
        "Y = Y.astype('int')\n",
        "print( X.shape , Y.shape)\n",
        "# X_train: (7352, 128, 9) and X_test: (2947, 128, 9) \n",
        "\n",
        "Class_numbers_unique = np.unique(Y)\n",
        "NUM_CLASSES =np.max( Y )+1\n",
        "print( ' NUM_CLASSES: ' , NUM_CLASSES  )\n",
        "print( ' number of unique classes: ' , Class_numbers_unique.shape)\n",
        "# find the number of classes in one fold\n",
        "class_size = np.zeros((NUM_CLASSES))\n",
        "for i in Class_numbers_unique:\n",
        "    class_size[i] = np.sum(Y == i)\n",
        "print('Train class sizes: ' , class_size, 'Total: ' , np.sum( class_size))\n",
        "\n",
        "########## remove non-exisitent labels\n",
        "########## relabel data with new sequential labels\n",
        "labels_dictionary = {}; new_Y= np.zeros(Y.shape).astype('int');  new_label = 0\n",
        "for i in range(len(Y)):\n",
        "  label = Y[i]\n",
        "  if not( str(label) in labels_dictionary.keys()) :\n",
        "    labels_dictionary[str(label)] = new_label\n",
        "    new_label = new_label +1\n",
        "  new_Y[i] = labels_dictionary[str(label)]\n",
        "\n",
        "Class_numbers_unique = np.unique(new_Y)\n",
        "NUM_CLASSES = np.max( new_Y )+1\n",
        "print( ' NUM_CLASSES: ' , NUM_CLASSES  )\n",
        "print( ' number of unique classes: ' , Class_numbers_unique.shape)\n",
        "# find the number of classes in one fold\n",
        "class_size = np.zeros((NUM_CLASSES))\n",
        "for i in range(NUM_CLASSES):\n",
        "    class_size[i] = np.sum(new_Y == i)\n",
        "print('Train class sizes: ' , class_size, 'Total: ' , np.sum( class_size))\n",
        "\n",
        "Y = new_Y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "65L9iM8kysmn",
        "outputId": "6d54da0d-7909-41d6-a006-a79d32dd435a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x360 with 6 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# ###########################################\n",
        "# # plot ECG beats and CWT images\n",
        "# #######################################\n",
        "\n",
        "def split_indices_per_label(y):\n",
        "    number_of_classes = max( y ) - 1\n",
        "    indicies_per_label = [[] for x in range(0,number_of_classes)]\n",
        "    # loop over the six labels\n",
        "    for i in range(number_of_classes): \n",
        "        indicies_per_label[i] = np.where(y == i)[0]\n",
        "    return indicies_per_label\n",
        "\n",
        "def plot_cwt_coeffs_per_label(X, label_indicies,  signal, sample, scales, wavelet):\n",
        "    \n",
        "    fig, axs = plt.subplots(nrows=2, ncols=3, sharex=True, sharey=True, figsize=(12,5))\n",
        "    \n",
        "    for ax, indices in zip(axs.flat, label_indicies):\n",
        "        # apply  PyWavelets continuous wavelet transfromation function\n",
        "        coeffs, freqs = pywt.cwt(X[indices[sample],:], scales, wavelet = wavelet)\n",
        "        # create scalogram\n",
        "        ax.imshow(coeffs, cmap = 'coolwarm', aspect = 'auto')\n",
        "        ax.set_title(str(indices[sample]))\n",
        "        ax.spines['right'].set_visible(False)\n",
        "        ax.spines['top'].set_visible(False)\n",
        "        ax.set_ylabel('Scale')\n",
        "        ax.set_xlabel('Time')\n",
        "        plt.tight_layout()\n",
        "\n",
        "# list of list of sample indicies per activity\n",
        "train_labels_indicies = split_indices_per_label(Y)\n",
        "\n",
        "#signal indicies: 0 = body acc x, 1 = body acc y, 2 = body acc z, 3 = body gyro x, 4 = body gyro y, 5 = body gyro z, 6 = total acc x, 7 = total acc y, 8 = total acc z\n",
        "signal = 3 # signal index\n",
        "sample = 0 # sample index of each label indicies list\n",
        "scales = np.arange(1, 65) # range of scales\n",
        "wavelets = [ \"mexh\"  , \"morl\"  , \"gaus5\" ] # mother wavelet\n",
        "\n",
        "plot_cwt_coeffs_per_label(X, train_labels_indicies,  signal, sample, scales, wavelet=wavelets[2])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NxbxcpdyoBP-"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "id": "SUbSInjBSuIj",
        "outputId": "7dda97ef-ca8e-45d8-ed18-556f41d54819"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "dict_keys(['__header__', '__version__', '__globals__', 'hbs'])\n",
            "(2, 199)\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "###########################################\n",
        "# Visulize one ECg heartbeat\n",
        "#######################################\n",
        "subject_number = 0 ; beat_number = 0 ;\n",
        "\n",
        "mat = scipy.io.loadmat(dataset_path+'hb_p9_s1.mat')\n",
        "# X = mat['X']; y = mat['y']; NUM_CLASSES = int(y.max()+1); print(NUM_CLASSES)\n",
        "print(mat.keys())\n",
        "h = mat['hbs']\n",
        "print(h.shape)\n",
        "\n",
        "heartbeats = h[0 , subject_number]; heartbeat  = heartbeats[beat_number]  #heartbeat_CWT = X[beat_number] ; heartbeat_label = Y[beat_number]\n",
        "heartbeat_label =  h[1 , subject_number][0][0]-1\n",
        "\n",
        "fig, axs = plt.subplots(nrows=1, ncols=4, figsize=(20,5))\n",
        "axs[0].plot( heartbeat )\n",
        "axs[0].set_title('Label: ' + str(heartbeat_label))\n",
        "axs[0].spines['right'].set_visible(False)\n",
        "axs[0].spines['top'].set_visible(False)\n",
        "axs[0].set_ylabel('Scale')\n",
        "axs[0].set_xlabel('Time')\n",
        "\n",
        "# range of scales from 1 to n_scales\n",
        "n_scales =  128; scales = np.arange(1, n_scales + 1) \n",
        "wavelets = [ \"mexh\"  , \"morl\"  , \"gaus5\" ]\n",
        "for i in range(1,4):\n",
        "  # continuous wavelet transform wavelet = \"mexh\"  # mexh, morl, gaus8, gaus4, 'sym5', 'coif5'\n",
        "  coeffs1, freqs = pywt.cwt(heartbeat, scales, wavelet = wavelets[i-1]  ) \n",
        "  # create scalogram\n",
        "  axs[i].imshow(coeffs1, cmap = 'coolwarm', aspect = 'auto')\n",
        "  axs[i].set_title(  wavelets[i-1]  )\n",
        "  axs[i].spines['right'].set_visible(False)\n",
        "  axs[i].spines['top'].set_visible(False)\n",
        "  axs[i].set_ylabel('Scale')\n",
        "  axs[i].set_xlabel('Time')\n",
        "\n",
        "plt.tight_layout()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0-jwwycNnNJX",
        "outputId": "64511def-1d42-438e-8fda-27019f27aa50"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f6970fa2910>]"
            ]
          },
          "execution_count": 46,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "axs[0].plot(  heartbeat )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D9MfvAxvAMw4"
      },
      "outputs": [],
      "source": [
        "# import pickle\n",
        "# from sklearn.preprocessing import RobustScaler\n",
        "# import numpy as np\n",
        "# from sklearn.model_selection import train_test_split\n",
        "\n",
        "\n",
        "# dataset_path = '/content/gdrive/MyDrive/000research/13-INF2168-02-R/Data-ECG/PP-HB-199/'\n",
        "\n",
        "# mat = scipy.io.loadmat(dataset_path+'sig_R_s1.mat')\n",
        "# # X = mat['X']; y = mat['y']; NUM_CLASSES = int(y.max()+1); print(NUM_CLASSES)\n",
        "# print(mat.keys())\n",
        "# h = mat['sig']\n",
        "# print(h[0].shape , h[1].shape, h[2].shape)\n",
        "# signals = h[0]; Rpeaks = h[1]; subject_index = h[2]\n",
        "# number_of_subjects = subject_index.shape[0]\n",
        "# print(signals[0][0].shape , Rpeaks[0][0].shape, subject_index[0].shape)\n",
        "# S0= signals[0]; S1 = S0[0,0]; S2 = S1[0]\n",
        "# signal_length = S2.shape[0]\n",
        "# X = np.zeros( (signal_length)   ) ; R = -1*np.ones( (100)   )\n",
        "# Y = np.array( [0]  ); \n",
        "# label= 0; \n",
        "# for i in range(number_of_subjects):\n",
        "#   S0= signals[i]; R0 = Rpeaks[i]\n",
        "#   number_of_records = S0.shape[1]\n",
        "#   for j in range(number_of_records):\n",
        "#     S1 = S0[0,j]; S2 = S1[0]\n",
        "#     # print(S2.shape)\n",
        "#     X = np.vstack((X , S2))\n",
        "#     R1 = R0[0,j]; R2 = R1[0]\n",
        "#     # print(R2.shape)\n",
        "#     R3 = np.pad(R2, (0, 100 - len(R2)%100), 'constant')\n",
        "#     R = np.vstack((R , R3))\n",
        "\n",
        "#   labels = label * np.ones( (  number_of_records  ));     \n",
        "#   Y = np.hstack((Y ,  labels.astype('int')   ))\n",
        "#   label = label + 1\n",
        "\n",
        "# NUM_CLASSES = int(label); print(NUM_CLASSES) \n",
        "\n",
        "# X = X[1:]; R = R[1:]; Y = Y[1:] \n",
        "# # data = {\"signal\": X, \"r_peaks\": R , \"categories\": Y}\n",
        "# # print( data[\"signal\"].shape , data[\"r_peaks\"].shape  , data[\"categories\"].shape)\n",
        "\n",
        "# signals_and_peak = np.hstack( (X , R )  ); del X, R\n",
        "# # split data\n",
        "# X_trn, X_tst, Y_trn, Y_tst = train_test_split(signals_and_peak, Y, test_size=0.2, random_state=42)\n",
        "\n",
        "# train_data = {\"signal\": X_trn[:,:-100] , \"r_peaks\": X_trn[:,-100:] , \"categories\": Y_trn}\n",
        "# test_data = {\"signal\": X_tst[:,:-100] , \"r_peaks\": X_tst[:,-100:] , \"categories\": Y_tst}\n",
        "\n",
        "# print( train_data[\"signal\"].shape , train_data[\"r_peaks\"].shape  , train_data[\"categories\"].shape)\n",
        "# print( test_data[\"signal\"].shape , test_data[\"r_peaks\"].shape  , test_data[\"categories\"].shape)\n",
        "# del X_trn, X_tst, Y_trn, Y_tst\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sfcnRdlBtRH1"
      },
      "outputs": [],
      "source": [
        "# ###########################################\n",
        "# # Load data and extract CWT to convert to images\n",
        "# #######################################\n",
        "# def my_worker(X, r_peaks,category , wavelet, scales, sampling_period):\n",
        "#   wavelet = \"mexh\"  # mexh, morl, gaus8, gaus4\n",
        "#   coeffs1, frequencies = pywt.cwt(X, scales, \"mexh\", sampling_period)\n",
        "#   coeffs2, frequencies = pywt.cwt(X, scales, \"gaus8\", sampling_period)\n",
        "#   coeffs3, frequencies = pywt.cwt(X, scales, \"morl\", sampling_period)\n",
        "#   # r_peaks, categories = data[\"r_peaks\"], data[\"categories\"]\n",
        "#   # for remove inter-patient variation\n",
        "#   avg_rri = np.mean(np.diff(r_peaks))\n",
        "#   before, after = 90, 110;\n",
        "#   x1, x2, y, groups = [], [], [], []\n",
        "#   for i in range(len(r_peaks)):\n",
        "#       # print( r_peaks[i] )\n",
        "#       if i == 0 or i == len(r_peaks) - 1:\n",
        "#           continue\n",
        "#       if (r_peaks[i]==0):\n",
        "#         break\n",
        "\n",
        "#       # cv2.resize is used to sampling the scalogram to (100 x100)\n",
        "#       img = np.zeros((3 , 64, 64))\n",
        "#       img[0] = cv2.resize(coeffs1[:, r_peaks[i] - before: r_peaks[i] + after], (64, 64))\n",
        "#       img[1] = cv2.resize(coeffs2[:, r_peaks[i] - before: r_peaks[i] + after], (64, 64))\n",
        "#       img[2] = cv2.resize(coeffs3[:, r_peaks[i] - before: r_peaks[i] + after], (64, 64))\n",
        "#       x1.append( img )\n",
        "#       x2.append([\n",
        "#           r_peaks[i] - r_peaks[i - 1] - avg_rri,  # previous RR Interval\n",
        "#           r_peaks[i + 1] - r_peaks[i] - avg_rri,  # post RR Interval\n",
        "#           (r_peaks[i] - r_peaks[i - 1]) / (r_peaks[i + 1] - r_peaks[i]),  # ratio RR Interval\n",
        "#           np.mean(np.diff(r_peaks[np.maximum(i - 10, 0):i + 1])) - avg_rri  # local RR Interval\n",
        "#       ])\n",
        "#       y.append(category)\n",
        "#       # groups.append(data[\"record\"])\n",
        "\n",
        "#   return x1, x2, y #, groups\n",
        "\n",
        "# # def my_load_data(train_data , wavelet, scales, sampling_rate ):\n",
        "# def my_load_data(train_data, wavelet, scales, sampling_rate  ):\n",
        "#     # for training\n",
        "#     x1_train, x2_train, y_train, groups_train = [], [], [], []\n",
        "#     number_samples = len(train_data[\"signal\"])\n",
        "#     x1_train = np.zeros((1 , 3,64,64)); x2_train = np.zeros((1 , 4))\n",
        "#     y_train = np.zeros((1))\n",
        "#     for i in range(number_samples):\n",
        "#       if (i%100==0): print(i , end='')\n",
        "#       X = train_data[\"signal\"][i]; r_peaks = train_data[\"r_peaks\"][i].astype('int'); category = train_data[\"categories\"][i]\n",
        "#       x1, x2, y = my_worker(X, r_peaks,category , wavelet, scales, sampling_period)\n",
        "#       x1 = np.array(x1); x2 = np.array(x2);  y = np.array(y); \n",
        "#       # print( x1.shape)\n",
        "#       x1_train = np.vstack(( x1_train , x1 )); x2_train = np.vstack(( x2_train , x2 )); \n",
        "#       y_train = np.hstack(( y_train , y )); \n",
        "#     print('\\n')\n",
        "#       # print( x1[0].shape)\n",
        "#     #   x1_train.append(x1)\n",
        "#     #   x2_train.append(x2)\n",
        "#     #   y_train.append(y)\n",
        "#     #   # groups_train.append(groups)\n",
        "\n",
        "#     # x1_train = np.expand_dims(np.concatenate(x1_train, axis=0), axis=1).astype(np.float32)\n",
        "#     # # x1_train = np.concatenate(x1_train, axis=0).astype(np.float32)\n",
        "#     # x2_train = np.concatenate(x2_train, axis=0).astype(np.float32)\n",
        "#     # y_train = np.concatenate(y_train, axis=0).astype('int') #.astype(np.int64)\n",
        "#     # # groups_train = np.concatenate(groups_train, axis=0)\n",
        "#     # # normalization\n",
        "#     x1_train = x1_train[1:]; x2_train = x2_train[1:]; y_train = y_train[1:];\n",
        "#     return x1_train, x2_train, y_train\n",
        " \n",
        "\n",
        "# sampling_rate = 360\n",
        "# wavelet = \"mexh\"  # mexh, morl, gaus8, gaus4\n",
        "# scales = pywt.central_frequency(wavelet) * sampling_rate / np.arange(1, 101, 1)\n",
        "# sampling_period=1. / sampling_rate\n",
        "# # (x1_train, x2_train, y_train), (x1_test, x2_test, y_test) = my_load_data(data,\n",
        "# #     wavelet=wavelet, scales=scales, sampling_rate=sampling_rate  )\n",
        "# x1_train, x2_train, y_train = my_load_data(train_data,  wavelet=wavelet, scales=scales, sampling_rate=sampling_rate  )\n",
        "# x1_test, x2_test, y_test = my_load_data(test_data, wavelet=wavelet, scales=scales, sampling_rate=sampling_rate  )\n",
        "# scaler = RobustScaler()\n",
        "# x2_train = scaler.fit_transform(x2_train)\n",
        "# x2_test = scaler.transform(x2_test)\n",
        "# print( 'x1_train.shape: ' , x1_train.shape)\n",
        "# print( 'x2_train.shape: ' , x2_train.shape)\n",
        "# print( 'y_train.shape: ' , y_train.shape)\n",
        "\n",
        "# print(\"Data loaded successfully!\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7QoltvFOe6Ij"
      },
      "outputs": [],
      "source": [
        "# print( 'x1_train.shape: ' , x1_train.shape)\n",
        "# print( 'x2_train.shape: ' , x2_train.shape)\n",
        "# print( 'y_train.shape: ' , y_train.shape)\n",
        "# # find the number of classes in one fold\n",
        "# class_size = np.zeros((NUM_CLASSES))\n",
        "# for i in range(NUM_CLASSES):\n",
        "#     class_size[i] = np.sum(y_train == i)\n",
        "# print('Train class sizes: ' , class_size, 'Total: ' , np.sum( class_size))\n",
        "# # find the number of classes in one fold\n",
        "# class_size = np.zeros((NUM_CLASSES))\n",
        "# for i in range(NUM_CLASSES):\n",
        "#     class_size[i] = np.sum(y_test == i)\n",
        "# print('Test class sizes: ' , class_size,  'Total: ' , np.sum( class_size))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cyj8EvneAlyu",
        "outputId": "bba2ad01-7447-4a88-e329-0b05b955e769"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "dict_keys(['__header__', '__version__', '__globals__', 'hbs'])\n",
            "219\n",
            "(7239, 227) (7239,)\n"
          ]
        }
      ],
      "source": [
        "########################################################\n",
        "# No need to run this as results is already saved to hard disk\n",
        "# load ECG heartbeat samples\n",
        "# average each 10 consecutive beats\n",
        "########################################################\n",
        "\n",
        "dataset_path = '/content/gdrive/MyDrive/000research/13-INF2168-02-R/Data-ECG/PP-HB-199/'\n",
        "\n",
        "def load_ECG_beats(fname):\n",
        "    mat = scipy.io.loadmat(fname)\n",
        "    # X = mat['X']; y = mat['y']; NUM_CLASSES = int(y.max()+1); print(NUM_CLASSES)\n",
        "    print(mat.keys())\n",
        "    h = mat['hbs']\n",
        "    # print(h.shape)\n",
        "    number_of_subjects = h.shape[1]\n",
        "    # we have a problem with subject 145  h[0,145]  has shape 0,0\n",
        "    X = h[0 , 0] ; Y = np.zeros( ( X.shape[0])  ); \n",
        "    labels_dictionary = {}; new_label = 0\n",
        "    for s in range(number_of_subjects):\n",
        "      h0 = h[0 , s]; label = h[1 , s][0][0]-1\n",
        "      # print( 'heartbeats: ' , h0.shape , ' label: ', s)\n",
        "      # if h0.shape[0]>0:\n",
        "      number_of_heartbeats, number_of_samples = h0.shape\n",
        "      for j in range(number_of_heartbeats-5):\n",
        "        # average take 10 beats\n",
        "        upper_index = np.min( [  j+10 , number_of_heartbeats  ])\n",
        "        avg_beat = np.mean(  h0[j:upper_index, :] , axis=0 )\n",
        "        X = np.vstack((X , avg_beat))\n",
        "        Y = np.hstack((Y ,  label    ))\n",
        "        # print( h0.shape , label )\n",
        "        # label = label + 1\n",
        "    NUM_CLASSES = int(label); print(NUM_CLASSES) \n",
        "    Y = Y.astype('int')\n",
        "    print( X.shape , Y.shape)\n",
        "    # X_train: (7352, 128, 9) and X_test: (2947, 128, 9) \n",
        "    return X, Y\n",
        "\n",
        "data_path = '/content/gdrive/MyDrive/000research/13-INF2168-02-R/colab_code/ECG-Classification-Using-CNN-and-CWT/data_CWT/'\n",
        "\n",
        "fname = dataset_path+'hb_p9_s1.mat'; X , Y = load_ECG_beats(fname)\n",
        "np.savez( data_path+'session01.npz' ,  X=X, Y= Y      )\n",
        "\n",
        "# fname = dataset_path+'hb_p9_s2.mat'; X , Y = load_ECG_beats(fname)\n",
        "# np.savez( data_path+'session02.npz' ,  X=X, Y= Y      )\n",
        "\n",
        "# fname = dataset_path+'hb_p9_s3R.mat'; X , Y = load_ECG_beats(fname)\n",
        "# np.savez( data_path+'session03R.npz' ,  X=X, Y= Y      )\n",
        "\n",
        "# fname = dataset_path+'hb_p9_s3L.mat'; X , Y = load_ECG_beats(fname)\n",
        "# np.savez( data_path+'session03L.npz' ,  X=X, Y= Y      )\n",
        "\n",
        "# X = np.vstack((X0 , X )); Y = np.hstack(( Y0 , Y ))\n",
        "# print( X.shape , Y.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wnW3vkN0HFQ2",
        "outputId": "38534e82-05d9-41bb-c05f-88704e216f01"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([  0,   0,   0, ..., 151, 151, 151])"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pTLvjFUmSOY-"
      },
      "outputs": [],
      "source": [
        "########################################################\n",
        "# No need to run this as results is already saved to hard disk\n",
        "# load ECG heartbeat samples\n",
        "# average each 10 consecutive beats\n",
        "########################################################\n",
        "# new method here https://towardsdatascience.com/multiple-time-series-classification-by-using-continuous-wavelet-transformation-d29df97c0442\n",
        "discrete_wavelets = ['db5', 'sym5', 'coif5', 'bior2.4']\n",
        "continuous_wavelets = ['mexh', 'morl', 'cgau5', 'gaus5']\n",
        "\n",
        "def create_cwt_images(X, n_scales, rescale_size, wavelet_name = \"morl\"):\n",
        "    n_samples = X.shape[0] \n",
        "    n_signals = 3 # X.shape[2] \n",
        "    print( 'n_samples: ', n_samples,  'n_signals: ' ,  n_signals  )\n",
        "    # range of scales from 1 to n_scales\n",
        "    scales = np.arange(1, n_scales + 1) \n",
        "    \n",
        "    # pre allocate array\n",
        "    X_cwt = np.ndarray(shape=(n_samples, rescale_size, rescale_size, n_signals ), dtype = 'float32')\n",
        "    signal = 1\n",
        "    for sample in range(n_samples):\n",
        "        if sample % 1000 == 0:\n",
        "            print(sample)\n",
        "        # for signal in range(n_signals):\n",
        "        serie = X[sample , :] # X[sample, :, signal]\n",
        "        # continuous wavelet transform wavelet = \"mexh\"  # mexh, morl, gaus8, gaus4, 'sym5', 'coif5'\n",
        "        coeffs1, freqs = pywt.cwt(serie, scales, wavelet = \"mexh\") \n",
        "        coeffs2, freqs = pywt.cwt(serie, scales, wavelet = \"morl\")\n",
        "        coeffs3, freqs = pywt.cwt(serie, scales, wavelet = \"gaus5\")\n",
        "        # resize the 2D cwt coeffs\n",
        "        X_cwt[sample, :,: , 0] = resize(coeffs1, (rescale_size, rescale_size), mode = 'constant')\n",
        "        X_cwt[sample, :,: , 1] = resize(coeffs2, (rescale_size, rescale_size), mode = 'constant')\n",
        "        X_cwt[sample, :,: , 2] = resize(coeffs3, (rescale_size, rescale_size), mode = 'constant')\n",
        "    # X_cwt  = np.squeeze(X_cwt)\n",
        "    return X_cwt\n",
        "  \n",
        "# amount of pixels in X and Y \n",
        "rescale_size = 64\n",
        "# determine the max scale size\n",
        "n_scales = 64\n",
        "\n",
        "session_name = 'session01'\n",
        "# session_name = 'session02'\n",
        "# session_name = 'session03L'\n",
        "# session_name = 'session03R'\n",
        "\n",
        "# /content/gdrive/MyDrive/000research/13-INF2168-02-R/colab_code/ECG-Classification-Using-CNN-and-CWT/data_CWT/session03L.npz\n",
        "data_path = '/content/gdrive/MyDrive/000research/13-INF2168-02-R/colab_code/ECG-Classification-Using-CNN-and-CWT/data_CWT/'\n",
        "fname = data_path+session_name+'.npz'; \n",
        "D = np.load( fname  )\n",
        "X = D['X'];   Y = D['Y']\n",
        "print( X.shape , Y.shape)\n",
        "\n",
        "X_cwt = create_cwt_images(X, n_scales, rescale_size)\n",
        "print(f\"shapes (n_samples, x_img, y_img, z_img) of X_train_cwt: {X_cwt.shape}\")\n",
        "\n",
        "np.savez( data_path+session_name+ '_CWT.npz' , X = X_cwt , Y = Y  )\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vT8LjzdQTgor",
        "outputId": "48a8b54b-cb5e-4f23-95f9-bf2f3855906a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(7239, 64, 64, 3) (7239,)\n"
          ]
        }
      ],
      "source": [
        "########################################################\n",
        "# load CWT features/images of heartbeats from any session\n",
        "# uncomment the session you want\n",
        "# \n",
        "########################################################\n",
        "\n",
        "session_name = 'session01'\n",
        "# session_name = 'session02'\n",
        "# session_name = 'session03L'\n",
        "# session_name = 'session03R'\n",
        "data_path = '/content/gdrive/MyDrive/000research/13-INF2168-02-R/colab_code/ECG-Classification-Using-CNN-and-CWT/data_CWT/'\n",
        "fname = data_path+session_name+ '_CWT.npz'\n",
        "D = np.load( fname  )\n",
        "X = D['X'];   Y = D['Y']\n",
        "print( X.shape , Y.shape)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TNVqf7yYbocc"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2SfET2cuLOg1"
      },
      "outputs": [],
      "source": [
        "###########################################\n",
        "# Prepare data for visualization \n",
        "#######################################\n",
        "import torch\n",
        "import cv2\n",
        "\n",
        "def make_sym_log(X):\n",
        "  rows, cols, depth = X.shape;  Z = np.zeros( X.shape )\n",
        "  for r in range(rows):\n",
        "    for c in range(cols):\n",
        "      for d in range(depth):\n",
        "        Z[r,c, d] = np.log( X[r,c, d]  +1 ) if X[r,c, d]>=0 else np.log( -X[r,c, d]  +1 )\n",
        "  return Z\n",
        "\n",
        "def convert_to_RGB(X):\n",
        "  fig = plt.figure(frameon=False)\n",
        "  ax = plt.Axes(fig, [0., 0., 1., 1.]); ax.set_axis_off(); fig.add_axes(ax)\n",
        "  ax.imshow(X[:,:,0], aspect='equal')\n",
        "  fig.savefig('band0.png', dpi=128, bbox_inches='tight',  pad_inches=0)\n",
        "\n",
        "  fig = plt.figure(frameon=False)\n",
        "  ax = plt.Axes(fig, [0., 0., 1., 1.]); ax.set_axis_off(); fig.add_axes(ax)\n",
        "  ax.imshow(X[:,:,1], aspect='equal')\n",
        "  fig.savefig('band1.png', dpi=128, bbox_inches='tight',  pad_inches=0)\n",
        "\n",
        "  fig = plt.figure(frameon=False)\n",
        "  ax = plt.Axes(fig, [0., 0., 1., 1.]); ax.set_axis_off(); fig.add_axes(ax)\n",
        "  ax.imshow(X[:,:,2], aspect='equal')\n",
        "  fig.savefig('band2.png', dpi=128, bbox_inches='tight',  pad_inches=0)\n",
        "\n",
        "  band0 = cv2.imread('band0.png', cv2.IMREAD_UNCHANGED)\n",
        "  # print('Original Dimensions : ',band0.shape)\n",
        "  # width = 64; height = 64\n",
        "  # dim = (width, height)\n",
        "  # # resize image\n",
        "  # resized = cv2.resize(img, dim, interpolation = cv2.INTER_AREA)\n",
        "  # print('Resized Dimensions : ',resized.shape)\n",
        "  band1 = cv2.imread('band1.png', cv2.IMREAD_UNCHANGED)\n",
        "  band2 = cv2.imread('band2.png', cv2.IMREAD_UNCHANGED)\n",
        "  newRGBImage = cv2.merge((band0[:,:,0],band1[:,:,0],band2[:,:,0]))\n",
        "  return newRGBImage\n",
        "\n",
        "\n",
        "# Visualize some examples\n",
        "batch = 10; plots_path = '/content/gdrive/MyDrive/000research/13-INF2168-02-R/colab_code/ECG-Classification-Using-CNN-and-CWT/plots/'\n",
        "NUM_IMAGES = 12; sample_images = []; sample_labels= []; Z = np.zeros( X.shape )\n",
        "for idx in range(NUM_IMAGES):\n",
        "  rnd_index = np.random.randint(len(Y))\n",
        "  # SYMMETRIC LOG Y=Logarithm(X+1) if X>0 Y=-logarithm(-X+1) if X<0\n",
        "  Z = convert_to_RGB(   X[rnd_index]    );\n",
        "  file_name = 'Label%03d'%Y[ rnd_index ]  + '_B%02d'%batch + '.png'\n",
        "  cv2.imwrite( plots_path+file_name , Z    )\n",
        "  sample_images.append( torch.tensor(    Z   )  )\n",
        "  sample_labels.append(  str( Y[ rnd_index ] ) )\n",
        "  \n",
        "sample_images = torch.stack(  sample_images , dim=0)\n",
        "# sample_images = np.array( sample_images  )\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 716
        },
        "id": "CMjJbl54Ldvt",
        "outputId": "19b2c358-39e8-45fa-f7f2-263fe1b36a07"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 6 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "###########################################\n",
        "# Now visulize the CWT images\n",
        "#######################################\n",
        "fig = plt.figure(figsize=(10,10))\n",
        "for i in range(6):\n",
        "  plt.subplot(3,2,i+1); \n",
        "  plt.tight_layout()\n",
        "  img = sample_images[i]; \n",
        "  # img = torch.squeeze(img[:,:,0])\n",
        "  plt.imshow(img,  interpolation='none' )\n",
        "  plt.title(   sample_labels[i]     )\n",
        "  plt.xticks([])\n",
        "  plt.yticks([])\n",
        "# fig"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1jN7SW7iWMbc"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "njWkr1QhUj-P",
        "outputId": "f7edff3d-d30b-4767-8241-60dc0dcc8d34"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(7239, 64, 64, 3) (7239,)\n",
            " NUM_CLASSES:  199\n",
            " number of unique classes:  (199,)\n",
            "Train class sizes:  [ 53.  35.  27.  25.  28.  33.  30.  23.  29.  24.  28.  30.  28.  28.\n",
            "  42.  35.  24.  35.  23.  36.  30.  18.  25.  25.  34.  25.  26.  37.\n",
            "  28.  29.  24.  41.  21.  19.  25.  28.  28.  39.  27.  34.  28.  20.\n",
            "  25.  37.  26.  26.  18.  15.  18.  30.  23.  26.  30.  17.  27.  33.\n",
            "  37.  39.  34.  39.  42.  35.  34.  31.  29.  32.  27.  32.  40.  37.\n",
            "  32.  27.  35.  26.  31.  28.  24.  30.  30.  20.  24.  21.  24.  27.\n",
            "  25.  23.  25.  26.  39.  35.  33.  32.  25.  25.  30.  28.  25.  20.\n",
            "  29.  38.  30.  26.  41.  29.  27.  30.  40.  30.  39.  33.  40.  24.\n",
            "  35.  26.  27.  23.  23.  36.  32.  26.  20.  27.  31.  34.  27.  32.\n",
            "  21.  27.  22.  30.  25.  30.  35.  25.  27.  32.  24.  27.  30.  28.\n",
            "  27.  29.  39.  19.  23.  23.  31.  36.  23.  37.  33.  25.  26.  23.\n",
            "  30.  26.  34.  24.  32.  25.  31.  37.  27.  33.  26.  22.  23.  28.\n",
            "  67.  61.  64.  39.  48.  61.  69.  54.  27.  87.  86.  78.  87.  70.\n",
            "  58.  56.  88.  52. 126.  63. 114.  89. 124.  63. 109.  49.  51.  96.\n",
            " 110. 121. 106.] Total:  7239.0\n",
            "(7069, 64, 64, 3) (7069,)\n",
            " number of unique classes:  (199,)\n",
            "(14308, 64, 64, 3) (14308,)\n",
            " NUM_CLASSES:  199\n",
            " number of unique classes:  (199,)\n",
            "Train class sizes:  [126.  59.  58.  51.  55.  68.  59.  42.  60.  43.  57.  56.  56.  69.\n",
            "  77.  68.  55.  63.  43.  65.  57.  37.  48.  48.  76.  52.  55.  70.\n",
            "  55.  61.  47.  73.  45.  42.  53.  58.  49.  76.  55.  65.  66.  47.\n",
            "  54.  73.  55.  51.  37.  30.  38.  64.  51.  55.  64.  36.  57.  68.\n",
            "  63.  67.  65.  76.  78.  72.  66.  63.  62.  66.  65.  58.  73.  67.\n",
            "  57.  54.  72.  51.  61.  58.  54.  61.  59.  50.  57.  44.  48.  53.\n",
            "  49.  44.  50.  58.  73.  70.  68.  65.  51.  49.  62.  52.  52.  42.\n",
            "  51.  65.  53.  50.  84.  59.  60.  70.  81.  64.  81.  69.  77.  60.\n",
            "  55.  56.  55.  45.  45.  64.  64.  65.  53.  45.  63.  65.  48.  73.\n",
            "  44.  55.  48.  60.  48.  52.  66.  58.  51.  62.  44.  54.  54.  56.\n",
            "  55.  75.  72.  46.  44.  48.  59.  67.  45.  66.  55.  66.  57.  45.\n",
            "  56.  62.  68.  60.  62.  60.  65.  64.  54.  72.  46.  39.  43.  58.\n",
            " 138. 140. 147.  75.  92. 134. 126. 120. 104. 178. 157. 149. 151. 101.\n",
            " 133. 112. 160.  95. 207. 136. 201. 156. 205. 129. 188. 130. 144. 164.\n",
            " 177. 191. 184.] Total:  14308.0\n",
            "Data splits:  (11446, 64, 64, 3) (11446,) (2862, 64, 64, 3) (2862,)\n"
          ]
        }
      ],
      "source": [
        "###########################################\n",
        "# Load session 1 and 2 data and merge them\n",
        "# I noticed here that labels are incomplete\n",
        "# I had to reassign new consecutive and common labels \n",
        "#######################################\n",
        "data_path = '/content/gdrive/MyDrive/000research/13-INF2168-02-R/colab_code/ECG-Classification-Using-CNN-and-CWT/data_CWT/'\n",
        "D = np.load( data_path+'session01'+ '_CWT.npz'  ); X = D['X'];   Y = D['Y']\n",
        "print( X.shape , Y.shape)\n",
        "\n",
        "labels_dictionary = {}; new_label = 0\n",
        "for i in range(len(Y)):\n",
        "  label = Y[i]\n",
        "  if not( str(label) in labels_dictionary.keys()) :\n",
        "    labels_dictionary[str(label)] = new_label\n",
        "    new_label = new_label +1\n",
        "  Y[i] = labels_dictionary[str(label)]\n",
        "\n",
        "Class_numbers_unique = np.unique(Y)\n",
        "NUM_CLASSES =np.max( Y )+1\n",
        "print( ' NUM_CLASSES: ' , NUM_CLASSES  )\n",
        "print( ' number of unique classes: ' , Class_numbers_unique.shape)\n",
        "# find the number of classes in one fold\n",
        "class_size = np.zeros((NUM_CLASSES))\n",
        "for i in range(NUM_CLASSES):\n",
        "    class_size[i] = np.sum(Y == i)\n",
        "print('Train class sizes: ' , class_size, 'Total: ' , np.sum( class_size))\n",
        "\n",
        "############### now load session2 data\n",
        "D = np.load( data_path+'session02'+ '_CWT.npz'  ); print( D['X'].shape , D['Y'].shape)\n",
        "X2 = D['X'];   Y2 = D['Y']\n",
        "\n",
        "# change labels for session 2\n",
        "Class_numbers_unique = np.unique(Y); new_Y = np.zeros(Y2.shape)\n",
        "print( ' number of unique classes: ' , Class_numbers_unique.shape)\n",
        "for i in range(len(Y2)):\n",
        "  old_label = str(Y2[i])\n",
        "  new_label = labels_dictionary[ old_label  ]\n",
        "  # print( old_label  , new_label   )\n",
        "  new_Y[i] = int(  new_label  )\n",
        "Y2 = new_Y\n",
        "\n",
        "# Merge two sets\n",
        "X = np.vstack(( X ,  X2) );   Y = np.hstack(( Y ,  Y2 )) ;\n",
        "print( X.shape , Y.shape)\n",
        "\n",
        "Class_numbers_unique = np.unique(Y)\n",
        "NUM_CLASSES = int(np.max( Y )) + 1\n",
        "print( ' NUM_CLASSES: ' , NUM_CLASSES  )\n",
        "print( ' number of unique classes: ' , Class_numbers_unique.shape)\n",
        "# find the number of classes in one fold\n",
        "class_size = np.zeros((NUM_CLASSES))\n",
        "for i in range(NUM_CLASSES):\n",
        "    class_size[i] = np.sum(Y == i)\n",
        "print('Train class sizes: ' , class_size, 'Total: ' , np.sum( class_size))\n",
        "\n",
        "# Split data\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=42)\n",
        "print( 'Data splits: ' , X_train.shape , Y_train.shape , X_test.shape , Y_test.shape)\n",
        "# Data splits:  (12984, 227) (12984,) (3246, 227) (3246,)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kqhy_JQ5advO",
        "outputId": "a0094617-09e5-426f-e6c8-12b421f5b1af"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Data splits:  (11446, 64, 64, 3) (11446,) (2862, 64, 64, 3) (2862,)\n",
            "(11446, 64, 64, 3) (11446,)\n",
            "(2862, 64, 64, 3) (2862,)\n",
            "(11446, 64, 64, 3) (11446,)\n",
            "(2862, 64, 64, 3) (2862,)\n"
          ]
        }
      ],
      "source": [
        "###########################################\n",
        "# make a new data split\n",
        "# and convert types to float and integer\n",
        "#######################################\n",
        "# Split data\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=42)\n",
        "print( 'Data splits: ' , X_train.shape , Y_train.shape , X_test.shape , Y_test.shape)\n",
        "# Data splits:  (12984, 227) (12984,) (3246, 227) (3246,)\n",
        "\n",
        "print( X_train.shape , Y_train.shape)\n",
        "print( X_test.shape , Y_test.shape)\n",
        "X_train = X_train.astype('float32')\n",
        "X_test = X_test.astype('float32')\n",
        "Y_train = Y_train.astype('int')\n",
        "Y_test = Y_test.astype('int')\n",
        "\n",
        "# Y_train = keras.utils.to_categorical(Y_train, NUM_CLASSES)\n",
        "# Y_test = keras.utils.to_categorical(Y_test, NUM_CLASSES)\n",
        "\n",
        "print( X_train.shape , Y_train.shape)\n",
        "print( X_test.shape , Y_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jZ46iW7siS5J",
        "outputId": "c20906bd-288d-47a1-d363-8f9da824f280"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(7239, 64, 64, 3) (7239,)\n",
            " NUM_CLASSES:  199\n",
            " number of unique classes:  (199,)\n",
            "Session 1: Train class sizes:  [ 53.  35.  27.  25.  28.  33.  30.  23.  29.  24.  28.  30.  28.  28.\n",
            "  42.  35.  24.  35.  23.  36.  30.  18.  25.  25.  34.  25.  26.  37.\n",
            "  28.  29.  24.  41.  21.  19.  25.  28.  28.  39.  27.  34.  28.  20.\n",
            "  25.  37.  26.  26.  18.  15.  18.  30.  23.  26.  30.  17.  27.  33.\n",
            "  37.  39.  34.  39.  42.  35.  34.  31.  29.  32.  27.  32.  40.  37.\n",
            "  32.  27.  35.  26.  31.  28.  24.  30.  30.  20.  24.  21.  24.  27.\n",
            "  25.  23.  25.  26.  39.  35.  33.  32.  25.  25.  30.  28.  25.  20.\n",
            "  29.  38.  30.  26.  41.  29.  27.  30.  40.  30.  39.  33.  40.  24.\n",
            "  35.  26.  27.  23.  23.  36.  32.  26.  20.  27.  31.  34.  27.  32.\n",
            "  21.  27.  22.  30.  25.  30.  35.  25.  27.  32.  24.  27.  30.  28.\n",
            "  27.  29.  39.  19.  23.  23.  31.  36.  23.  37.  33.  25.  26.  23.\n",
            "  30.  26.  34.  24.  32.  25.  31.  37.  27.  33.  26.  22.  23.  28.\n",
            "  67.  61.  64.  39.  48.  61.  69.  54.  27.  87.  86.  78.  87.  70.\n",
            "  58.  56.  88.  52. 126.  63. 114.  89. 124.  63. 109.  49.  51.  96.\n",
            " 110. 121. 106.] Total:  7239.0\n",
            "(7069, 64, 64, 3) (7069,)\n",
            " number of unique classes:  (199,)\n",
            " NUM_CLASSES:  199\n",
            " number of unique classes:  (199,)\n",
            "Session 2: Train class sizes:  [73. 24. 31. 26. 27. 35. 29. 19. 31. 19. 29. 26. 28. 41. 35. 33. 31. 28.\n",
            " 20. 29. 27. 19. 23. 23. 42. 27. 29. 33. 27. 32. 23. 32. 24. 23. 28. 30.\n",
            " 21. 37. 28. 31. 38. 27. 29. 36. 29. 25. 19. 15. 20. 34. 28. 29. 34. 19.\n",
            " 30. 35. 26. 28. 31. 37. 36. 37. 32. 32. 33. 34. 38. 26. 33. 30. 25. 27.\n",
            " 37. 25. 30. 30. 30. 31. 29. 30. 33. 23. 24. 26. 24. 21. 25. 32. 34. 35.\n",
            " 35. 33. 26. 24. 32. 24. 27. 22. 22. 27. 23. 24. 43. 30. 33. 40. 41. 34.\n",
            " 42. 36. 37. 36. 20. 30. 28. 22. 22. 28. 32. 39. 33. 18. 32. 31. 21. 41.\n",
            " 23. 28. 26. 30. 23. 22. 31. 33. 24. 30. 20. 27. 24. 28. 28. 46. 33. 27.\n",
            " 21. 25. 28. 31. 22. 29. 22. 41. 31. 22. 26. 36. 34. 36. 30. 35. 34. 27.\n",
            " 27. 39. 20. 17. 20. 30. 71. 79. 83. 36. 44. 73. 57. 66. 77. 91. 71. 71.\n",
            " 64. 31. 75. 56. 72. 43. 81. 73. 87. 67. 81. 66. 79. 81. 93. 68. 67. 70.\n",
            " 78.] Total:  7069.0\n"
          ]
        }
      ],
      "source": [
        "###########################################\n",
        "# Load session 1 and 2 data and fix the labeles, DO NOT MERGE BECAUSE WE WILL TRAIN ON S1 and TEST ON S2\n",
        "# I noticed here that labels are incomplete\n",
        "# I had to reassign new consecutive and common labels \n",
        "#######################################\n",
        "data_path = '/content/gdrive/MyDrive/000research/13-INF2168-02-R/colab_code/ECG-Classification-Using-CNN-and-CWT/data_CWT/'\n",
        "D = np.load( data_path+'session01'+ '_CWT.npz'  ); X1 = D['X'];   Y1 = D['Y']\n",
        "print( X1.shape , Y1.shape)\n",
        "\n",
        "labels_dictionary = {}; new_label = 0; new_Y = np.zeros(Y1.shape)\n",
        "for i in range(len(Y1)):\n",
        "  label = Y1[i]\n",
        "  if not( str(label) in labels_dictionary.keys()) :\n",
        "    labels_dictionary[str(label)] = new_label\n",
        "    new_label = new_label +1\n",
        "  new_Y[i] = labels_dictionary[str(label)]\n",
        "Y1 = new_Y\n",
        "\n",
        "Class_numbers_unique = np.unique(Y1); \n",
        "NUM_CLASSES = new_label\n",
        "print( ' NUM_CLASSES: ' , NUM_CLASSES  )\n",
        "print( ' number of unique classes: ' , Class_numbers_unique.shape)\n",
        "# find the number of classes in one fold\n",
        "class_size = np.zeros((NUM_CLASSES))\n",
        "for i in range(NUM_CLASSES):\n",
        "    class_size[i] = np.sum(Y1 == i)\n",
        "print('Session 1: Train class sizes: ' , class_size, 'Total: ' , np.sum( class_size))\n",
        "\n",
        "############### now load session2 data\n",
        "D = np.load( data_path+'session02'+ '_CWT.npz'  ); print( D['X'].shape , D['Y'].shape)\n",
        "X2 = D['X'];   Y2 = D['Y']\n",
        "\n",
        "# change labels for session 2\n",
        "Class_numbers_unique = np.unique(Y2); new_Y = np.zeros(Y2.shape)\n",
        "print( ' number of unique classes: ' , Class_numbers_unique.shape)\n",
        "for i in range(len(Y2)):\n",
        "  old_label = str(Y2[i])\n",
        "  new_label = labels_dictionary[ old_label  ]\n",
        "  # print( old_label  , new_label   )\n",
        "  new_Y[i] = int(  new_label  )\n",
        "Y2 = new_Y\n",
        "\n",
        "Class_numbers_unique = np.unique(Y2)\n",
        "print( ' NUM_CLASSES: ' , NUM_CLASSES  )\n",
        "print( ' number of unique classes: ' , Class_numbers_unique.shape)\n",
        "# find the number of classes in one fold\n",
        "class_size = np.zeros((NUM_CLASSES))\n",
        "for i in range(NUM_CLASSES):\n",
        "    class_size[i] = np.sum(Y2 == i)\n",
        "print('Session 2: Train class sizes: ' , class_size, 'Total: ' , np.sum( class_size))\n",
        "\n",
        "\n",
        "################################\n",
        "#  set session as train and session 2 as test\n",
        "X_train, Y_train, X_test, Y_test = X1, Y1, X2, Y2\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hPma8ZROWTzD",
        "outputId": "3ed2acdb-6d38-4086-ba97-447eea9f97f9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "TensorFlow version: 2.8.2\n",
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_2 (Conv2D)           (None, 64, 64, 32)        2432      \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 32, 32, 32)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 32, 32, 64)        51264     \n",
            "                                                                 \n",
            " max_pooling2d_3 (MaxPooling  (None, 16, 16, 64)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten_1 (Flatten)         (None, 16384)             0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 256)               4194560   \n",
            "                                                                 \n",
            " batch_normalization_2 (Batc  (None, 256)              1024      \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " re_lu_2 (ReLU)              (None, 256)               0         \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 128)               32896     \n",
            "                                                                 \n",
            " batch_normalization_3 (Batc  (None, 128)              512       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " re_lu_3 (ReLU)              (None, 128)               0         \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 199)               25671     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4,308,359\n",
            "Trainable params: 4,307,591\n",
            "Non-trainable params: 768\n",
            "_________________________________________________________________\n",
            "None\n"
          ]
        }
      ],
      "source": [
        "###########################################\n",
        "# Build sequential model \n",
        "# and train\n",
        "#######################################\n",
        "import tensorflow as tf\n",
        "print(f\"TensorFlow version: {tf.__version__}\")\n",
        "#import keras (high level API) wiht tensorflow as backend\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Activation, Flatten, BatchNormalization, ReLU\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint\n",
        "\n",
        "def build_cnn_model0(activation, input_shape , num_of_clases = 6 ):\n",
        "    model = Sequential()\n",
        "    \n",
        "    # 2 Convolution layer with Max polling\n",
        "    model.add(Conv2D(32, 5, activation = activation, padding = 'same', input_shape = input_shape))\n",
        "    model.add(MaxPooling2D())\n",
        "    model.add(Conv2D(64, 5, activation = activation, padding = 'same', kernel_initializer = \"he_normal\"))\n",
        "    model.add(MaxPooling2D())  \n",
        "    model.add(Flatten())\n",
        "    \n",
        "    # 3 Full connected layer\n",
        "    # model.add(Dense(256, activation = activation, kernel_initializer = \"he_normal\"))\n",
        "    model.add(Dense(256,  kernel_initializer = \"he_normal\"))\n",
        "    model.add( BatchNormalization()    )\n",
        "    model.add(  ReLU()  )\n",
        "    # model.add(Dense(128, activation = activation, kernel_initializer = \"he_normal\"))\n",
        "    model.add(Dense(128,  kernel_initializer = \"he_normal\"))\n",
        "    model.add( BatchNormalization()    )\n",
        "    model.add(  ReLU()  )\n",
        "    model.add(Dense(num_of_clases, activation = 'softmax')) # 6 classes\n",
        "    \n",
        "    # summarize the model\n",
        "    print(model.summary())\n",
        "    return model\n",
        "\n",
        "def build_cnn_model(activation, input_shape , num_of_clases = 6 ):\n",
        "    model = Sequential()\n",
        "    \n",
        "    # 2 Convolution layer with Max polling\n",
        "    model.add(Conv2D(32, 5, activation = activation, padding = 'same', input_shape = input_shape))\n",
        "    model.add(MaxPooling2D())\n",
        "    model.add(Conv2D(64, 5, activation = activation, padding = 'same', kernel_initializer = \"he_normal\"))\n",
        "    model.add(MaxPooling2D())  \n",
        "    model.add(Flatten())\n",
        "    \n",
        "    # 3 Full connected layer\n",
        "    # model.add(Dense(256, activation = activation, kernel_initializer = \"he_normal\"))\n",
        "    model.add(Dense(256,  kernel_initializer = \"he_normal\"))\n",
        "    model.add( BatchNormalization()    )\n",
        "    model.add(  ReLU()  )\n",
        "    # model.add(Dense(128, activation = activation, kernel_initializer = \"he_normal\"))\n",
        "    model.add(Dense(128,  kernel_initializer = \"he_normal\"))\n",
        "    model.add( BatchNormalization()    )\n",
        "    model.add(  ReLU()  )\n",
        "    model.add(Dense(num_of_clases, activation = 'softmax')) # 6 classes\n",
        "    \n",
        "    # summarize the model\n",
        "    print(model.summary())\n",
        "    return model\n",
        "\n",
        "def compile_and_fit_model(model, X_train, y_train, X_test, y_test, batch_size, n_epochs):\n",
        "   \n",
        "    return model, history\n",
        "\n",
        "# shape of the input images\n",
        "input_shape = (X_train.shape[1], X_train.shape[2], X_train.shape[3])\n",
        "# create cnn model\n",
        "cnn_model = build_cnn_model(\"relu\", input_shape , NUM_CLASSES)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "etAbnERa1HNC",
        "outputId": "1e94de85-2841-416f-e977-e15625652695"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(7239, 64, 64, 3) (7239,)\n",
            "(7069, 64, 64, 3) (7069,)\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(Adam, self).__init__(name, **kwargs)\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "905/905 [==============================] - 112s 105ms/step - loss: 2.5722 - sparse_categorical_accuracy: 0.3820 - val_loss: 5.5836 - val_sparse_categorical_accuracy: 0.2105\n",
            "Epoch 2/20\n",
            "905/905 [==============================] - 86s 95ms/step - loss: 0.7722 - sparse_categorical_accuracy: 0.7699 - val_loss: 5.8029 - val_sparse_categorical_accuracy: 0.2860\n",
            "Epoch 3/20\n",
            "905/905 [==============================] - 87s 96ms/step - loss: 0.3884 - sparse_categorical_accuracy: 0.8866 - val_loss: 5.8330 - val_sparse_categorical_accuracy: 0.2753\n",
            "Epoch 4/20\n",
            "905/905 [==============================] - 87s 96ms/step - loss: 0.3098 - sparse_categorical_accuracy: 0.9058 - val_loss: 5.6736 - val_sparse_categorical_accuracy: 0.2555\n",
            "Epoch 5/20\n",
            "905/905 [==============================] - 87s 96ms/step - loss: 0.2487 - sparse_categorical_accuracy: 0.9290 - val_loss: 6.1848 - val_sparse_categorical_accuracy: 0.3230\n",
            "Epoch 6/20\n",
            "905/905 [==============================] - 92s 101ms/step - loss: 0.2003 - sparse_categorical_accuracy: 0.9428 - val_loss: 6.1212 - val_sparse_categorical_accuracy: 0.3091\n",
            "Epoch 7/20\n",
            "905/905 [==============================] - 85s 94ms/step - loss: 0.1802 - sparse_categorical_accuracy: 0.9485 - val_loss: 5.8775 - val_sparse_categorical_accuracy: 0.2843\n",
            "Epoch 8/20\n",
            "905/905 [==============================] - 91s 101ms/step - loss: 0.1596 - sparse_categorical_accuracy: 0.9577 - val_loss: 6.7743 - val_sparse_categorical_accuracy: 0.2645\n",
            "Epoch 9/20\n",
            "905/905 [==============================] - 87s 96ms/step - loss: 0.1061 - sparse_categorical_accuracy: 0.9729 - val_loss: 6.4427 - val_sparse_categorical_accuracy: 0.1294\n",
            "Epoch 10/20\n",
            "905/905 [==============================] - 86s 95ms/step - loss: 0.1438 - sparse_categorical_accuracy: 0.9644 - val_loss: 5.5315 - val_sparse_categorical_accuracy: 0.3392\n",
            "Epoch 11/20\n",
            "905/905 [==============================] - 87s 96ms/step - loss: 0.1011 - sparse_categorical_accuracy: 0.9728 - val_loss: 5.5061 - val_sparse_categorical_accuracy: 0.3532\n",
            "Epoch 12/20\n",
            "905/905 [==============================] - 88s 97ms/step - loss: 0.1075 - sparse_categorical_accuracy: 0.9725 - val_loss: 5.0812 - val_sparse_categorical_accuracy: 0.3375\n",
            "Epoch 13/20\n",
            "905/905 [==============================] - 87s 96ms/step - loss: 0.0910 - sparse_categorical_accuracy: 0.9762 - val_loss: 6.2287 - val_sparse_categorical_accuracy: 0.2982\n",
            "Epoch 14/20\n",
            "905/905 [==============================] - 93s 103ms/step - loss: 0.0922 - sparse_categorical_accuracy: 0.9761 - val_loss: 7.1470 - val_sparse_categorical_accuracy: 0.2084\n",
            "Epoch 15/20\n",
            "905/905 [==============================] - 88s 97ms/step - loss: 0.0686 - sparse_categorical_accuracy: 0.9858 - val_loss: 5.3707 - val_sparse_categorical_accuracy: 0.4039\n",
            "Epoch 16/20\n",
            "905/905 [==============================] - 86s 95ms/step - loss: 0.1116 - sparse_categorical_accuracy: 0.9749 - val_loss: 6.0015 - val_sparse_categorical_accuracy: 0.3372\n",
            "Epoch 17/20\n",
            "905/905 [==============================] - 92s 102ms/step - loss: 0.0726 - sparse_categorical_accuracy: 0.9827 - val_loss: 5.8200 - val_sparse_categorical_accuracy: 0.3606\n",
            "Epoch 18/20\n",
            "905/905 [==============================] - 91s 100ms/step - loss: 0.0538 - sparse_categorical_accuracy: 0.9855 - val_loss: 5.9402 - val_sparse_categorical_accuracy: 0.3157\n",
            "Epoch 19/20\n",
            "905/905 [==============================] - 91s 101ms/step - loss: 0.0695 - sparse_categorical_accuracy: 0.9815 - val_loss: 5.6561 - val_sparse_categorical_accuracy: 0.3696\n",
            "Epoch 20/20\n",
            "905/905 [==============================] - 85s 94ms/step - loss: 0.0818 - sparse_categorical_accuracy: 0.9811 - val_loss: 6.5235 - val_sparse_categorical_accuracy: 0.3041\n",
            "Epoch 1/40\n",
            "905/905 [==============================] - 105s 97ms/step - loss: 0.0089 - sparse_categorical_accuracy: 0.9978 - val_loss: 5.6817 - val_sparse_categorical_accuracy: 0.3986\n",
            "Epoch 2/40\n",
            "905/905 [==============================] - 84s 93ms/step - loss: 0.0015 - sparse_categorical_accuracy: 0.9996 - val_loss: 5.5786 - val_sparse_categorical_accuracy: 0.3989\n",
            "Epoch 3/40\n",
            "905/905 [==============================] - 90s 99ms/step - loss: 8.9073e-04 - sparse_categorical_accuracy: 0.9997 - val_loss: 5.5993 - val_sparse_categorical_accuracy: 0.3967\n",
            "Epoch 4/40\n",
            "905/905 [==============================] - 91s 101ms/step - loss: 0.0024 - sparse_categorical_accuracy: 0.9996 - val_loss: 5.5870 - val_sparse_categorical_accuracy: 0.3999\n",
            "Epoch 5/40\n",
            "905/905 [==============================] - 91s 100ms/step - loss: 9.0662e-04 - sparse_categorical_accuracy: 0.9999 - val_loss: 5.5540 - val_sparse_categorical_accuracy: 0.4115\n",
            "Epoch 6/40\n",
            "905/905 [==============================] - 84s 93ms/step - loss: 6.7977e-04 - sparse_categorical_accuracy: 0.9997 - val_loss: 5.4830 - val_sparse_categorical_accuracy: 0.4111\n",
            "Epoch 7/40\n",
            "905/905 [==============================] - 87s 96ms/step - loss: 0.0013 - sparse_categorical_accuracy: 0.9994 - val_loss: 5.5222 - val_sparse_categorical_accuracy: 0.4090\n",
            "Epoch 8/40\n",
            "905/905 [==============================] - 90s 100ms/step - loss: 2.3078e-04 - sparse_categorical_accuracy: 1.0000 - val_loss: 5.6416 - val_sparse_categorical_accuracy: 0.4049\n",
            "Epoch 9/40\n",
            "905/905 [==============================] - 90s 100ms/step - loss: 8.9172e-04 - sparse_categorical_accuracy: 0.9997 - val_loss: 5.6881 - val_sparse_categorical_accuracy: 0.3761\n",
            "Epoch 10/40\n",
            "905/905 [==============================] - 85s 94ms/step - loss: 2.8418e-04 - sparse_categorical_accuracy: 1.0000 - val_loss: 5.6500 - val_sparse_categorical_accuracy: 0.4013\n",
            "Epoch 11/40\n",
            "905/905 [==============================] - 86s 95ms/step - loss: 2.0212e-04 - sparse_categorical_accuracy: 1.0000 - val_loss: 5.6080 - val_sparse_categorical_accuracy: 0.4112\n",
            "Epoch 12/40\n",
            "905/905 [==============================] - 85s 94ms/step - loss: 1.9371e-04 - sparse_categorical_accuracy: 1.0000 - val_loss: 5.6709 - val_sparse_categorical_accuracy: 0.4098\n",
            "Epoch 13/40\n",
            "905/905 [==============================] - 93s 102ms/step - loss: 5.7980e-05 - sparse_categorical_accuracy: 1.0000 - val_loss: 5.6885 - val_sparse_categorical_accuracy: 0.4114\n",
            "Epoch 14/40\n",
            "905/905 [==============================] - 91s 101ms/step - loss: 8.0507e-05 - sparse_categorical_accuracy: 1.0000 - val_loss: 5.7157 - val_sparse_categorical_accuracy: 0.4143\n",
            "Epoch 15/40\n",
            "905/905 [==============================] - 89s 99ms/step - loss: 8.7205e-05 - sparse_categorical_accuracy: 1.0000 - val_loss: 5.8179 - val_sparse_categorical_accuracy: 0.4108\n",
            "Epoch 16/40\n",
            "905/905 [==============================] - 83s 92ms/step - loss: 7.5877e-05 - sparse_categorical_accuracy: 1.0000 - val_loss: 5.7795 - val_sparse_categorical_accuracy: 0.4067\n",
            "Epoch 17/40\n",
            "905/905 [==============================] - 85s 94ms/step - loss: 5.6505e-04 - sparse_categorical_accuracy: 0.9997 - val_loss: 5.8030 - val_sparse_categorical_accuracy: 0.4104\n",
            "Epoch 18/40\n",
            "905/905 [==============================] - 81s 90ms/step - loss: 9.0174e-05 - sparse_categorical_accuracy: 1.0000 - val_loss: 5.7924 - val_sparse_categorical_accuracy: 0.4125\n",
            "Epoch 18: early stopping\n",
            "Accuracy: 41.25%\n",
            "(5997, 64, 64, 3) (5997,)\n",
            "(5997, 64, 64, 3) (5997,)\n",
            "Accuracy: 35.35%\n",
            "(3679, 64, 64, 3) (3679,)\n",
            "(3679, 64, 64, 3) (3679,)\n",
            "Accuracy: 29.63%\n"
          ]
        }
      ],
      "source": [
        "# train cnn model\n",
        "from tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping , ModelCheckpoint\n",
        "import seaborn as sns\n",
        "from sklearn import metrics\n",
        "\n",
        "# trained_cnn_model, cnn_history = compile_and_fit_model(cnn_model, X_train, Y_train, X_test, Y_test, batch_size=8, n_epochs=25)\n",
        "batch_size=8; n_epochs=60\n",
        "\n",
        "# compile the model\n",
        "cnn_model.compile(\n",
        "    optimizer=tf.keras.optimizers.Adam(lr=0.001),\n",
        "    loss='sparse_categorical_crossentropy',\n",
        "    metrics=['sparse_categorical_accuracy'])\n",
        "# define callbacks\n",
        "# callbacks = [ModelCheckpoint(filepath='best_model.h5', monitor='val_sparse_categorical_accuracy', save_best_only=True)]  \n",
        "# fit the model\n",
        "history1 = cnn_model.fit(x=X_train,\n",
        "                    y=Y_train,\n",
        "                    batch_size=batch_size,\n",
        "                    epochs=20,\n",
        "                    verbose=1,\n",
        "                    validation_data=(X_test, Y_test))\n",
        "                    # callbacks=callbacks,\n",
        "\n",
        "es = EarlyStopping(monitor='loss', mode='min', verbose=1, patience=5)\n",
        "cnn_model.compile(\n",
        "    optimizer=tf.keras.optimizers.Adam(lr=0.0001),\n",
        "    loss='sparse_categorical_crossentropy',\n",
        "    metrics=['sparse_categorical_accuracy'])\n",
        "# define callbacks\n",
        "# callbacks = [ModelCheckpoint(filepath='best_model.h5', monitor='val_sparse_categorical_accuracy', save_best_only=True)]  \n",
        "# fit the model\n",
        "history2 = cnn_model.fit(x=X_train,\n",
        "                    y=Y_train,\n",
        "                    batch_size=batch_size,\n",
        "                    epochs=n_epochs-20,\n",
        "                    verbose=1,\n",
        "                    validation_data=(X_test, Y_test),\n",
        "                    callbacks=[es])\n",
        "\n",
        "# make predictions for test data\n",
        "Y_pred = cnn_model.predict(X_test)\n",
        "Y_pred_classes = np.argmax( Y_pred , axis =1  )\n",
        "# determine the total accuracy \n",
        "accuracy = metrics.accuracy_score(Y_test, Y_pred_classes)\n",
        "print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kan5hwqm24d8",
        "outputId": "0943ce18-425c-4b29-f5fd-51d6d6c59703"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 100.00%\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "96.79"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "###########################################\n",
        "# Evaluate model on test set\n",
        "# tried to plot confucion matrix but it does not look good\n",
        "#######################################\n",
        "import seaborn as sns\n",
        "from sklearn import metrics\n",
        "\n",
        "LABEL_NAMES = []\n",
        "for i in range(NUM_CLASSES):\n",
        "  LABEL_NAMES.append( str(i))\n",
        "\n",
        "def create_confusion_matrix(y_pred, y_test):    \n",
        "    #calculate the confusion matrix\n",
        "    confmat = metrics.confusion_matrix(y_true=y_test, y_pred=y_pred)\n",
        "    \n",
        "    fig, ax = plt.subplots(figsize=(7,7))\n",
        "    ax.imshow(confmat, cmap=plt.cm.Blues, alpha=0.5)\n",
        "\n",
        "    n_labels = len(LABEL_NAMES)\n",
        "    ax.set_xticks(np.arange(n_labels))\n",
        "    ax.set_yticks(np.arange(n_labels))\n",
        "    ax.set_xticklabels(LABEL_NAMES)\n",
        "    ax.set_yticklabels(LABEL_NAMES)\n",
        "\n",
        "    # rotate the tick labels and set their alignment.\n",
        "    plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\", rotation_mode=\"anchor\")\n",
        "    # loop over data dimensions and create text annotations.\n",
        "    for i in range(confmat.shape[0]):\n",
        "        for j in range(confmat.shape[1]):\n",
        "            ax.text(x=i, y=j, s=confmat[i, j], va='center', ha='center')\n",
        "    \n",
        "    # avoid that the first and last row cut in half\n",
        "    bottom, top = ax.get_ylim()\n",
        "    ax.set_ylim(bottom + 0.5, top - 0.5)\n",
        "    \n",
        "    ax.set_title(\"Confusion Matrix\")\n",
        "    ax.set_xlabel('Predicted Label')\n",
        "    ax.set_ylabel('True Label')\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "# make predictions for test data\n",
        "Y_pred = cnn_model.predict(X_test)\n",
        "Y_pred_classes = np.argmax( Y_pred , axis =1  )\n",
        "# determine the total accuracy \n",
        "accuracy = metrics.accuracy_score(Y_test, Y_pred_classes)\n",
        "print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))\n",
        "\n",
        "# create_confusion_matrix(Y_pred_classes, Y_test)\n",
        "\n",
        "# obtained results \n",
        "# 98.56\n",
        "# 96.79\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fJgG0nQq4ijS",
        "outputId": "acbb2cb5-ec09-48bb-a36b-7b405af2e153"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(5997, 64, 64, 3) (5997,)\n",
            "(5997, 64, 64, 3) (5997,)\n",
            "Accuracy: 46.44%\n",
            "(3679, 64, 64, 3) (3679,)\n",
            "(3679, 64, 64, 3) (3679,)\n",
            "Accuracy: 31.48%\n"
          ]
        }
      ],
      "source": [
        "###########################################\n",
        "# Now load session 3 dataset\n",
        "# \n",
        "#######################################\n",
        "data_path = '/content/gdrive/MyDrive/000research/13-INF2168-02-R/colab_code/ECG-Classification-Using-CNN-and-CWT/data_CWT/'\n",
        "D = np.load( data_path+'session03R'+ '_CWT.npz'  ); X = D['X'];   Y = D['Y']\n",
        "print( X.shape , Y.shape)\n",
        "\n",
        "for i in range(len(Y)):\n",
        "  old_label = str(  Y[i] )\n",
        "  new_label = labels_dictionary[ old_label  ]\n",
        "  Y[i] = new_label\n",
        "\n",
        "X = X.astype('float32')\n",
        "Y_test = Y.astype('int')\n",
        "print( X.shape , Y.shape)\n",
        "# make predictions for test data\n",
        "Y_pred = trained_cnn_model.predict(X)\n",
        "Y_pred_classes = np.argmax( Y_pred , axis =1  )\n",
        "# determine the total accuracy \n",
        "accuracy = metrics.accuracy_score(Y_test, Y_pred_classes)\n",
        "print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))\n",
        "\n",
        "data_path = '/content/gdrive/MyDrive/000research/13-INF2168-02-R/colab_code/ECG-Classification-Using-CNN-and-CWT/data_CWT/'\n",
        "D = np.load( data_path+'session03L'+ '_CWT.npz'  ); X = D['X'];   Y = D['Y']\n",
        "print( X.shape , Y.shape)\n",
        "for i in range(len(Y)):\n",
        "  old_label = str(  Y[i] )\n",
        "  new_label = labels_dictionary[ old_label  ]\n",
        "  Y[i] = new_label\n",
        "X = X.astype('float32')\n",
        "Y_test = Y.astype('int')\n",
        "print( X.shape , Y.shape)\n",
        "# make predictions for test data\n",
        "Y_pred = trained_cnn_model.predict(X)\n",
        "Y_pred_classes = np.argmax( Y_pred , axis =1  )\n",
        "# determine the total accuracy \n",
        "accuracy = metrics.accuracy_score(Y_test, Y_pred_classes)\n",
        "print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Un9EX0xX4imS"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bi1oNuLQ4ipC"
      },
      "outputs": [],
      "source": []
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z1D256cK4irz"
      },
      "outputs": [],
      "source": []
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jZ4v2leG4iuX"
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      "source": []
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4vnJXLqY4ixK"
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      "source": []
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ybwRZk8j4izz"
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      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "machine_shape": "hm",
      "provenance": [],
      "authorship_tag": "ABX9TyNnxCL4+/TUKFNhRYsGcAjp",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
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  "nbformat": 4,
  "nbformat_minor": 0
}